Developing crystallization processes for the production of pharmaceuticals that are both cost-effective and environmentally friendly is a highly complex challenge, since it is often the case that too many candidate systems exist for them to all be studied experimentally. The widespread use and high fidelity of systematic process modeling tools enable study of crystallization systems in silico prior to conducting experimental trials, thereby allowing rapid identification of promising processes toward accelerating drug development and waste minimization. Moreover, these tools can be implemented without determining crystallization kinetics, as each system can be cast as a simple solid–liquid equilibrium (SLE) problem using established thermodynamic models alone. Consequently, this paper explores the use of data-led SLE models to assess total plant cost and environmental impact of different solvent systems for crystallizing pregabalin (an anticonvulsant and anxiolytic drug for treating epilepsy, anxiety, and neuropathic pain), via reliable costing methodologies and environmental impact metrics (process mass intensity, scope-1 and -2 CO2 emissions). Methanol-water and isopropanol-water emerge as the best mixed solvent systems, both outperforming pure solvent options: the former achieves a 3.2% lower plant cost (but at 25% higher emissions) vs. the latter.
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Blair et al. (2026) studied this question.
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